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Raspbot V2📖
1.About Raspbot V2
  • 1. First trial
  • 2. Expansion board interface distribution
  • 3. About battery and charging
  • 4. Install servo PTZ
  • 5.Servo Gimbal Installation
  • 6. Important Notes (Read Before Starting)
2.Assembly
  • Installation Video
3.Car basic course
  • 0. Preparation before development
  • 1.Buzzer control
  • 2.Key reading
  • 3.RGB control
  • 4.Servo control (PTZ control)
  • 5.Motor control
  • 6.Printing ultrasonic data
  • 7.Tracking module status printing
  • 8.IR control key value printing
4.Car motion control course
  • 0.Mecanum wheel motion principle
  • 1.Car vertical movement
  • 2.Car diagonal movement
  • 3.Car sideways movement
  • 4.Car rotation movement
  • 5.Car patrol
5.Car advanced courses
  • 1.Infrared patrol car
  • 2.Ultrasonic avoid
  • 3.Ultrasonic follow
  • 4.Infrared tracking+ ultrasonic avoid
  • 5.Restricting
  • 6.Infrared remote control car
6.OpenCV courses
A. Getting Started with OpenCV
  • 1. Introduction to OpenCV
  • 2.OpenCV image reading and display
  • 3. OpenCV image writing
  • 4. OpenCV Image Quality
  • 5.OpenCV pixel operations
B. OpenCV geometric transformation
  • 1. Image scaling
  • 2. Image cutting
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine Transformation
  • 6. Image Rotation
  • 7. Perspective Transformation
C. OpenCV image processing and text line drawing
  • 1. Grayscale processing
  • 2. Image Binarization
  • 3. Edge Detection
  • 4. Line drawing
  • 5. Rectangle and circle drawing
  • 6. Text and image drawing
D. Image beautification
  • 1. Color Image Histogram
  • 2. Histogram Equalization
  • 3. Image repair
  • 4. Brightness enhancement
  • 5. Smooth and whiten
  • 6. Gaussian mean filtering
  • 7. Median Filter
E. Machine Learning
  • 1. KNN
  • 2. SVM
7.AI vision recognition course
  • 1.Camera driver tutorial
  • 2.Color recognition
  • 3.HSV value test
  • 4.Object Recognition
  • 5.QR code recognition
  • 6.Face recognition
  • 7.License Plate Recognition
  • 8. Human Pose Estimation
  • 9. Target detection
  • 10.yolov5-lite
  • 11. Gesture Recognition
  • 12. Face Detection
  • 13. Face effects
  • 14. 3D Object Recognition
  • 15. Paintbrush
  • 16. Finger control
8.AI vision interaction module
  • 1. Color tracking
  • 2. Color follows
  • 3. QR code recognition to control movement
  • 4. Face tracking
  • 5. Face following
  • 6. Visual tracking
  • 7. Garbage identification
  • 8. Track map Autopilot(purchased separately)
  • 9. Gesture following
9.Voice control basic course(For Superior Kit)new
  • 1.Voice control car movement
  • 2.Voice control RGB light
  • 3.Voice control color recognition
  • 4.Voice control autonomous driving
  • 5.Voice control car tracking
10.AI large model knowledge(For Superior Kit)new
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
  • 5.Configure AI large model(Required)
11.AI Large Model Development(For Superior Kit & Reference)new
  • 1.Register a model service provider account
  • 2. Configuring API-KEY(Optional)
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
  • 9. Custom wake-up response
  • 10. Core module testing tools
12.AI large basic course(For Superior Kit)new
  • 1.Free conversation
  • 2.Text-to-picture
  • 3.Scene description
  • 4.Video description
  • 5.Intelligent action choreography
  • 6.Dual-model agent application
13.ROS2-Robot chassis control course
  • 1. Robot information release
  • 2. Robot keyboard control
  • 3. URDF Model
14.ROS2-OpenCV course
  • 1.ROS+Opencv application
  • 2. AR Vision
  • 3. Mediapipe fun gameplay
  • 4. Gesture control car movement
  • 5. Gesture control car actions
  • 6. Palm control car movement
  • 7. Palm control servo PTZ
  • 8. Visual tracking
  • 9. AprilTag code recognition
  • 10. AprilTag tag code tracking
  • 11. AprilTag tag code follow
15.Development environment course
  • 1. Introduction to Raspberry Pi 5
  • 2. Image writing and backup
  • 3. Start the Raspberry Pi 5
  • 4. Remote Access
  • 5. Remote file transfer
  • 6. Common Linux commands and use of vim editor
  • 7. Install Raspbot V2 driver library
  • 8. Jupyter Lab environment construction
  • 9. Jupyter Lab plugin installation
  • 10. Basic use of Jupyter Widgets
16.Raspberry Pi basic settings
  • 1.Update and upgrade the operating system
  • 2. Introduction to raspi-config tool
  • 3. config.txt file description
  • 4. Network Configuration
  • 5. Set display resolution and rotation
  • 6. Set screen sleep
  • 7. Get real-time temperature of RPi
  • 8. Play audio and video
  • 9. Use USB Camera
  • 10.Use MIPI camera
17.Docker course
  • 1. Overview and installation
  • 2. Common commands
  • 3. Understand and publish images
  • 4. Docker data processing
  • 5. Enter docker container
18.ROS2 basic course
  • 1. Introduction to ROS2
  • 2.ROS2 install Humble
  • 3. ROS2 development environment
  • 4. ROS2 workspace
  • 5. ROS2 function package
  • 6. ROS2 node
  • 7.ROS2 topic communication
  • 8. ROS2 service communication
  • 9. ROS2 action communication
  • 10. ROS2 custom interface message
  • 11. ROS2 parameter service case
  • 12. ROS2 meta-function package
  • 13. ROS2 distributed communication
  • 14. ROS2 DDS
  • 15. ROS2 time related API
  • 16. ROS2 common command tools
  • 17. ROS2 rviz2 use
  • 18. ROS2 rqt toolbox
  • 19. ROS2 Launch startup file configuration
  • 20. ROS2 recording and playback tool
  • 21. ROS2 URDF model
  • 22. ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
Download
  • APP(iOS/Android)
  • Code
  • System_File
  • Instruction Manual
  • Hardware_Info
  • 3D_Model_File
  • ROS2_Basic_VM
Download ZIP

Welcome to Raspbot V2 repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

3. QR code recognition to control movement

3. QR code recognition to control movement

3. QR code recognition to control movement3.1. Experimental Objectives3.2. Experimental code3.3. Experimental Phenomenon

3.1. Experimental Objectives

We have a preliminary understanding of QR code technology and raspbot control hardware, and have realized the decoding of QR codes using OpenCV. Now, we try to combine QR codes with hardware. First, we use free online QR code making software to prepare 5 QR codes, each of which corresponds to a different function to realize the function of QR code control robot.

forward

back

left

right

stop

3.2. Experimental code

Source code path:

/home/pi/project_demo/08.AI_Visual_Interaction_Course/03.QRcode_recog_campaign/03_QRcode_recog_campaign.ipynb

​x
#bgr8转jpeg格式 bgr8 to jpeg format
import enum
import cv2

def bgr8_to_jpeg(value, quality=75):
    return bytes(cv2.imencode('.jpg', value)[1])
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import sys
sys.path.append('/home/pi/project_demo/lib')
#导入麦克纳姆小车驱动库 Import Mecanum Car Driver Library
from McLumk_Wheel_Sports import *
sys.path.append('/home/pi/software/oled_yahboom/')
from yahboom_oled import *
# 创建oled对象 Create an oled object
oled = Yahboom_OLED(debug=False)
# import the necessary packages
import numpy as np
import pyzbar.pyzbar as pyzbar
from PIL import Image
import ipywidgets.widgets as widgets
# from picamera2 import Picamera2, Preview
# import libcamera


image_widget = widgets.Image(format='jpeg', width=640, height=320)
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def servo_reset():
    bot.Ctrl_Servo(1,90)
    bot.Ctrl_Servo(2,25)
servo_reset()
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speed=60
def detect_control(info):
    if info == "forward":
        move_forward(speed)        #前进 forward
    elif info == "back":
        move_backward(speed)        #后退 back
    elif info == "left":
        rotate_left(speed)   #左旋 Rotate Left
    elif info == "right":
        rotate_right(speed)   #右旋 Rotate right
    elif info == "stop":         #停车 stop
        stop_robot() 
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display(image_widget)                                      #显示摄像头组件 Display camera components
def decodeDisplay(image):
    barcodes = pyzbar.decode(image)
    for barcode in barcodes:
        # 提取二维码的边界框的位置 Extract the position of the bounding box of the QR code
        # 画出图像中条形码的边界框 Draw the bounding box of the barcode in the image
        (x, y, w, h) = barcode.rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 225, 225), 2)

        # 提取二维码数据为字节对象,所以如果我们想在输出图像上 Extract the QR code data as a byte object and convert it into a string
        # 画出来,就需要先将它转换成字符串
        datatemp=0
        barcodeData = barcode.data.decode("utf-8")
        barcodeType = barcode.type

        # 绘出图像上条形码的数据和条形码类型 Draw the barcode data and barcode type on the image
        text = "{} ({})".format(barcodeData, barcodeType)
        cv2.putText(image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 225, 225), 2)
        
        # 向终端打印条形码数据和条形码类型 Print barcode data and barcode type to the terminal
        print("[INFO] Found {} barcode: {}".format(barcodeType, barcodeData))
        if(barcodeData!=datatemp):
            oled.clear()
            qrtype_str=f'[INFO] Found {barcodeType}'
            qrcode_str=f'barcode: {barcodeData}'
            oled.add_line(qrtype_str, 1)
            oled.add_line(qrcode_str, 3)
            oled.refresh()
            datatemp=barcodeData
        detect_control(barcodeData)
    return image

def detect():
    oled.init_oled_process() #初始化oled进程 Initialize oled process
    qrtype_str=f'[INFO] Found '
    qrcode_str=f'barcode: None'
    oled.add_line(qrtype_str, 1)
    oled.add_line(qrcode_str, 3)
    oled.refresh()
    camera = cv2.VideoCapture(0)
    camera.set(3, 320)
    camera.set(4, 240)
    camera.set(5, 30)  #设置帧率 Setting the frame rate
    # fourcc = cv2.VideoWriter_fourcc(*"MPEG")
    # camera.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
    # camera.set(cv2.CAP_PROP_BRIGHTNESS, 40) #设置亮度 -64 - 64  0.0 Set Brightness -64 - 64 0.0
    # camera.set(cv2.CAP_PROP_CONTRAST, 50) #设置对比度 -64 - 64  2.0 Set Contrast -64 - 64 2.0
    # camera.set(cv2.CAP_PROP_EXPOSURE, 156) #设置曝光值 1.0 - 5000  156.0 Set the exposure value 1.0 - 5000 156.0
    ret, frame = camera.read()
    
    # picam2 = Picamera2()  
    # camera_config = picam2.create_preview_configuration(main={"format":'RGB888',"size":(320,240)})
    # camera_config["transform"] = libcamera.Transform(hflip=1, vflip=1)
    # picam2.configure(camera_config) 
    # picam2.start() 
    # frame = picam2.capture_array()
    image_widget.value = bgr8_to_jpeg(frame)
    try:
        while True:
            # 读取当前帧 Read the current frame
            ret, frame = camera.read()
            #frame = picam2.capture_array()
            # 转为灰度图像 Convert to grayscale image
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            im = decodeDisplay(gray)
            image_widget.value = bgr8_to_jpeg(im)
            cv2.waitKey(5)
    except:
        #picam2.stop_preview()
        camera.release()
        # 恢复屏幕基础数据显示 Restore basic data display on screen
        os.system("python3 /home/pi/software/oled_yahboom/yahboom_oled.py &")

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detect()
stop_robot()
oled.init_oled_process() #初始化oled进程 Initialize oled process
# 恢复屏幕基础数据显示 Restore basic data display on screen
os.system("python3 /home/pi/software/oled_yahboom/yahboom_oled.py &")
print("Ending")     

3.3. Experimental Phenomenon

After the code block is run, we can put the QR code in front of the camera, and the car will move according to the content of the QR code.

Color follows
Face tracking

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